@property
    def _identifying_params(self) -> Mapping[str, Any]:
        """Get the identifying parameters."""
        return {
            **{"model_id": self.model_id},
            **{"model_kwargs": self.model_kwargs},
        }
    @property
    def _llm_type(self) -> str:
        """Return type of llm."""
        return "deepinfra"
    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
        """Call out to DeepInfra's inference API endpoint.
        Args:
            prompt: The prompt to pass into the model.
            stop: Optional list of stop words to use when generating.
        Returns:
            The string generated by the model.
        Example:
            .. code-block:: python
                response = di("Tell me a joke.")
        """
        _model_kwargs = self.model_kwargs or {}
        res = requests.post(
            f"https://api.deepinfra.com/v1/inference/{self.model_id}",
            headers={
                "Authorization": f"bearer {self.deepinfra_api_token}",
                "Content-Type": "application/json",
            },
            json={"input": prompt, **_model_kwargs},
        )
        if res.status_code != 200:
            raise ValueError("Error raised by inference API")
        text = res.json()[0]["generated_text"]
        if stop is not None:
            # I believe this is required since the stop tokens
            # are not enforced by the model parameters
            text = enforce_stop_tokens(text, stop)
        return text
By Harrison Chase
    
      © Copyright 2023, Harrison Chase.
      
  Last updated on Mar 22, 2023.